AUCTORES
Globalize your Research
Review Article | DOI: https://doi.org/10.31579/2766-2314/060
Department of Chemical and Biomolecular Engineering, National University of Singapore.
*Corresponding Author: Wenfa Ng, Department of Chemical and Biomolecular Engineering, National University of Singapore.
Citation: Wenfa Ng (2021) Evaluating the Potential of Applying Machine Learning Tools to Metabolic Pathway Optimization. J, Biotechnology and Bioprocessing 2(9); DOI: 10.31579/2766-2314/060
Copyright: © 2021, Wenfa Ng, This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Received: 05 October 2021 | Accepted: 18 October 2021 | Published: 28 October 2021
Keywords: pathway optimization, machine learning tools, enzyme activity prediction, promoter classification, expression tuning
Successful engineering of a microbial host for efficient production of a target product from a given substrate can be viewed as an extensive optimization task. Such a task involves the selection of high activity enzymes as well as their gene expression regulatory control elements (i.e., promoters and ribosome binding sites). Finally, there is also the need to tune expression of multiple genes along a heterologous pathway to relieve constraints from rate-limiting step and help reduce metabolic burden on cells from unnecessary over-expression of high activity enzymes. While the aforementioned tasks could be performed through combinatorial experiments, such an approach incurs significant cost, time and effort, which is a handicap that can be relieved by application of modern machine learning tools. Such tools could attempt to predict high activity enzymes from sequence, but they are currently most usefully applied in classifying strong promoters from weaker ones as well as combinatorial tuning of expression of multiple genes. This perspective reviews the application of machine learning tools to aid metabolic pathway optimization through identifying challenges in metabolic engineering that could be overcome with the help of machine learning tools.
Metabolic engineering sought to increase the production of small molecules using cellular metabolism that has been rewired through genetic engineering. Comprising a workflow that can be categorized into design-build-test-learn cycle, a typical metabolic engineering project would involve iterative design and test experiments aiming to improve the expression of desired genes and production of target metabolites. As such, many aspects of engineering a microbe for overproducing a metabolite can be characterized as an optimization problem. Specifically, a key concern in metabolic engineering has been the selection of enzymes with high activity under a broad range of conditions, and strong promoters and ribosome binding sites, and the tuning of expression of multiple genes [1-4]. These goals could not be achieved through rational selection, but only with trial and error experimentation. The latter is not desirable considering the time and effort involved, and this has sowed the seeds for introducing data-driven algorithmic approaches to the traditional confines of metabolic engineering.
Although optimization can be performed by various algorithms, the latest trend has been the application of machine learning tools in optimization problems. In general, machine learning algorithms sought to identify patterns hidden in large datasets, and this enabling feature has been used in different aspects of metabolic engineering such as pathway optimization. Fundamentally, machine learning algorithms builds a model from input data using an iterative cycle of parameter fitting to a curvilinear description of the data. The obtained machine learning model could subsequently be used in predicting properties of enzymes or pathways using certain inputs. This thus arrives at the core enabling feature of machine learning: it is an automated search for a set of mathematic descriptions that describe particular sets of data. Usually thought to be data intensive, machine learning tools could also be applied to small datasets with or without data augmentation [5-7], and this latter feature dovetails with the inherently small scale nature of many biological datasets.
Thus far, machine learning has been applied to many but not all aspects of metabolic engineering and pathway optimization. For example, machine learning has been utilised in reconstruction of metabolic model of a species [8-12]. De novo pathway engineering is another aspect that has benefited from application of machine learning tools [13]. Machine learning tools have also enabled the deciphering of kinetic parameters of enzymes from metabolomics data [14,15]. In the same vein, correlations between expression level and design of various gene expression control elements (e.g., promoter and ribosome binding site) has been sought using the tools of machine learning [16].
This article sought to review areas where machine learning has informed pathway optimization. These include: (i) selecting enzymes with the highest activity for a pathway, (ii) selecting promoters and ribosome binding site of appropriate strength for particular genes, and (iii) tuning the expression of multiple genes in a pathway (Figure 1). But, the journey marched by machine learning in metabolic pathway optimization remains incomplete. For example, opportunities exist in applying machine learning to predict the regulatory motifs of enzymes and pathway dynamics from multiomics data [17], as well as assessing the performance of microbial cell factory [18].
Essentially, a metabolic pathway comprises a set of reactions that transform a substrate into a product through a series of bond formation and cleavage. Both expert knowledge and retrobiosynthetic approaches could be used in developing this pathway [11]. In particular, machine learning methodologies have been successfully applied in retrobiosynthesis and is reviewed elsewhere [11, 19]. With a set of coupled sequential reactions in mind, the next step is in selecting the appropriate enzymes for performing the respective reactions. Recently, machine learning has helped refine gene annotation through better recognition of genomic signals such as polyadenylation signals and translational start site [20]. In particular, deep learning approaches have played an important role in dissecting the often convoluted signals from the genome in assigning gene function to sequence information [21-25], and is poised to help identify more enzyme candidates with suitable functions in a metabolic engineering project. The latter comes about due to enzyme promiscuity where some enzymes could be repurposed for other functions [26,27]. Usually, enzymes with the highest activities and performance are desired. But, given the plethora of similar enzymes in different species, how does one select the best performing enzyme for an application? Can machine learning help rule out some candidates that are unlikely to work? In addition, which performance measure should be the basis for optimization? Since enzyme performance can be described by turnover number, inhibitory concentration (Ki) and binding affinity between substrate and enzyme (Km), multiple parameters could be used in machine learning tasks for predicting enzyme performance with amino acid sequence as input. But, the challenge lies in relative lack of full set of characterization data for different enzymes in varied species. Such data are incomplete given the effort and resources needed to perform detailed biochemical assays for each substrate.
One example of applying machine learning to predicting enzyme activity is in using an ensemble of enzyme characteristics such as biochemical parameters and structure to inform enzyme catalytic turnover number, which is a proxy parameter for enzyme activity [28] (Figure 2). Correlations between catalytic turnover number and enzyme structure elucidated by the machine learning tools hold important implications for how structural biology could inform enzyme biochemistry [28]. Indeed, other studies have also corroborated that enzyme conformation can be reliably correlated with enzyme activity level [29,30]. Furthermore, it has been shown that sequence alone could not accurately describe enzyme activity [31]. However, a study has shown that combination of sequence information and structural descriptors of enzyme-substrate recognition is useful for predicting enzyme function and activity [31]. Hence, the current state-of-the-art in enzyme activity prediction remains firmly in the realm of structure-activity correlate, with attempts at extending the correlation to the sequence level meeting challenges at our inability to resolve the protein folding problem. But future advances in using machine learning to circumvent the protein structure prediction problem may ultimately tie the link between enzyme sequence and activity level.
Optimization of gene expression regulatory elements
Gene expression regulatory elements such as promoters and ribosome binding site (RBS) controls the level at which the heterologous genes could be expressed. To facilitate selection of appropriate promoter and RBS for tuning the expression of heterologous genes, a need exists to build a predictive model able to correlate promoter or RBS sequence with expression level.
Theoretically, building a machine learning model capable of predicting promoter or RBS strength from sequence information does not necessarily require accurate definition of promoter or RBS sequence, which remains a research topic [32]. But, if promoter sequences in a training dataset are accurate, this would reduce the noise in the model and afford more accurate prediction. Hence, the computational challenge in applying machine learning to promoter strength prediction lies in the identification of small snippets of nucleotide sequence that strongly correlates with expression level [33]. Currently, a commonly used method for extracting sequence features is position weight matrix [34], but the approach may not be transferable to different species [33]. Another problem with promoter strength prediction is the relative lack of data, particularly in cases where machine learning is applied to experimentally characterized promoters [35,36]. But, use of genome-wide RNA-seq data may provide sufficient data that significantly improves machine learning based predictions of promoter strength.
Typically, the input data for training are promoter or RBS sequence and expression level as measured by protein or mRNA transcripts abundance. Such data could be modelled by support vector machine algorithms [36,37] (Figure 3), but recently, deep learning methods have also been applied to the problem and have shown promising results [38,39]. One approach uses pseudo-dinucleotide composition coupled to CNN for both promoter identification and strength prediction in prokaryotic organisms [39]. The method demonstrated good performance compared to state-of-the-art methods, but it is still limited to classifying promoters into strong or weak promoters, which does not provide metabolic engineers with the ability to achieve fine-grained control over gene expression. Another approach took into consideration evolutionarily relationships between orthologous genes and showed that such a methodology provided better predictions of mRNA abundance from DNA sequence [40]. Overall, neural network-based approaches may not be the only way forward in promoter strength prediction, particularly in cases with small datasets. For example, kernel-based approaches such as support vector machines have provided better performance compared to artificial neural network in some instances [36].
In comparison to promoters, ribosome binding sites are more well-defined. This comes about due to the structure of gene regulatory region where ribosome binding sites (RBS) are downstream of the transcriptional start site (TSS), which could be experimentally defined by RNA-seq data [33]. Similar to promoters, RBS are important modulators of gene expression level given that it governs the strength of binding between the small subunit (SSU) of ribosome with the mRNA transcript obtained after transcription. In a recent study, machine learning tools were used in defining the RBS sequence-phenotype relationship, which forms the basis for predicting optimal RBS sequences for multi-gene pathway. Computational predictions were validated through experiments and demonstrated the approach’s utility in enabling screening of a large combination of RBS sequences for multi-gene pathway [41]. But, in general, correlation between RBS sequence and expression level may not be easily discernible by machine learning tools. For example, a recent study did not find strong correlation between experimental protein expression data and predicted RBS strength [42], thereby, indicating room for improvement in the application of machine learning to RBS strength prediction. One major hurdle in RBS strength prediction comes from the relatively small sequence space of these gene regulatory elements as RBS are inherently shorter than promoters. Lack of sufficient variability in expression level from the small RBS sequence set would thus severely hamper prediction of protein expression level from RBS sequence.
Expression of a heterologous gene in a cell incurs a metabolic burden. For long pathway comprising multiple genes, such metabolic burden may have a detrimental effect on cell growth. In other situations, there may be excessive expression of enzyme for a particular step of the pathway that may lead to depletion of an intermediate metabolite needed to maintain other critical pathways of the cell. Hence, a need exists in tuning the expression of individual gene in a pathway to ensure that only sufficient enzymes are expressed to enable proper functioning of the pathway and deliver higher yields, and preventing metabolic choke points from emerging. Tools available for tuning the expression of multiple genes in a pathway would be promoter and ribosome binding site. Since heterologous genes are typically expressed in an operon in prokaryotic hosts, ribosome binding site tuning are more often used in prokaryotes. On the other hand, need for individual promoter for each gene of the pathway in eukaryotic hosts meant that promoter tuning is as important as ribosome binding site tuning in eukaryotes. Combinatorial tuning of promoter and RBS may thus afford fine-grained control over gene expression in eukaryotes.
Conceptually, the problem of optimizing expression of individual genes in a pathway can be depicted as a search for optimal levels of individual enzyme in a gene expression landscape. Statistical design of experiments approach has been put to use in this endeavour, yielding promising results that reduce experiment effort [43,44]. However, such search for the optimal combination of promoter and RBS usually will not arrive at the global optimum. In particular, extent in which the gene expression landscape is sampled determines the likelihood in which an optimal could be obtained. As the number of tunable parameters (promoter and RBS) increases with each additional gene in the pathway, the optimization problem could quickly escalate in complexity and size beyond the search capability of conventional optimization algorithms.
One way to circumvent the problem is through employing machine learning to detect hidden mathematical relationships between different sampled points on the gene expression landscape where combinatorial pathway optimization experimentation help deliver the data points that feeds the machine learning algorithms (Figure 4). In a recent example, artificial neural networks are employed to glean relationships between product titer of different strains with different promoters in a combinatorial optimization exercise. Predictions from the machine learning algorithms were verified experimentally, thereby, demonstrating the utility of the approach [45]. Besides neural networks, support vector machines and other kernel-based approaches may also be useful for such applications. However, how well machine learning performs critically depends on the characteristics of the input dataset and its size. Better predictions would naturally come from a larger dataset, which places greater demand on experimentation in combinatorial tuning of expression of multiple genes. In addition, input data should also cover a wide range in order to achieve a large dynamic range for corresponding predictions of product yield, titer, and productivity. At a more fundamental level, much room exists for the utilization of machine learning approaches in combinatorial pathway optimization since most studies in the field still relies on statistical design of experiment or construction of smart libraries to expedite search for optimal gene expression level of a pathway [46,47]. Developing better methods to efficiently and cost-effectively generate the input data for training various machine learning models remain an important research topic.
Machine learning has been applied to many facets of metabolic engineering and pathway optimization. From selection of enzymes to tuning of gene regulatory elements, machine learning’s greatest strength has been the gleaning of hidden patterns in complex data set to help offer solutions in new situations through building a predictive mathematical model. Such automated tools significantly ease the burden on metabolic engineers in making critical decisions such as gene selection and promoter choice during pathway optimization. But, application of machine learning tools to metabolic engineering remain a significant challenge for the novice researcher. This is made even harder by the cryptic nature of machine learning algorithms. Thus, more resources may be provided to enable researchers to begin integrating these tools across the pathway development process.
The author declares no conflicts of interest.
No funding was used in this work.
Clearly Auctoresonline and particularly Psychology and Mental Health Care Journal is dedicated to improving health care services for individuals and populations. The editorial boards' ability to efficiently recognize and share the global importance of health literacy with a variety of stakeholders. Auctoresonline publishing platform can be used to facilitate of optimal client-based services and should be added to health care professionals' repertoire of evidence-based health care resources.
Journal of Clinical Cardiology and Cardiovascular Intervention The submission and review process was adequate. However I think that the publication total value should have been enlightened in early fases. Thank you for all.
Journal of Women Health Care and Issues By the present mail, I want to say thank to you and tour colleagues for facilitating my published article. Specially thank you for the peer review process, support from the editorial office. I appreciate positively the quality of your journal.
Journal of Clinical Research and Reports I would be very delighted to submit my testimonial regarding the reviewer board and the editorial office. The reviewer board were accurate and helpful regarding any modifications for my manuscript. And the editorial office were very helpful and supportive in contacting and monitoring with any update and offering help. It was my pleasure to contribute with your promising Journal and I am looking forward for more collaboration.
We would like to thank the Journal of Thoracic Disease and Cardiothoracic Surgery because of the services they provided us for our articles. The peer-review process was done in a very excellent time manner, and the opinions of the reviewers helped us to improve our manuscript further. The editorial office had an outstanding correspondence with us and guided us in many ways. During a hard time of the pandemic that is affecting every one of us tremendously, the editorial office helped us make everything easier for publishing scientific work. Hope for a more scientific relationship with your Journal.
The peer-review process which consisted high quality queries on the paper. I did answer six reviewers’ questions and comments before the paper was accepted. The support from the editorial office is excellent.
Journal of Neuroscience and Neurological Surgery. I had the experience of publishing a research article recently. The whole process was simple from submission to publication. The reviewers made specific and valuable recommendations and corrections that improved the quality of my publication. I strongly recommend this Journal.
Dr. Katarzyna Byczkowska My testimonial covering: "The peer review process is quick and effective. The support from the editorial office is very professional and friendly. Quality of the Clinical Cardiology and Cardiovascular Interventions is scientific and publishes ground-breaking research on cardiology that is useful for other professionals in the field.
Thank you most sincerely, with regard to the support you have given in relation to the reviewing process and the processing of my article entitled "Large Cell Neuroendocrine Carcinoma of The Prostate Gland: A Review and Update" for publication in your esteemed Journal, Journal of Cancer Research and Cellular Therapeutics". The editorial team has been very supportive.
Testimony of Journal of Clinical Otorhinolaryngology: work with your Reviews has been a educational and constructive experience. The editorial office were very helpful and supportive. It was a pleasure to contribute to your Journal.
Dr. Bernard Terkimbi Utoo, I am happy to publish my scientific work in Journal of Women Health Care and Issues (JWHCI). The manuscript submission was seamless and peer review process was top notch. I was amazed that 4 reviewers worked on the manuscript which made it a highly technical, standard and excellent quality paper. I appreciate the format and consideration for the APC as well as the speed of publication. It is my pleasure to continue with this scientific relationship with the esteem JWHCI.
This is an acknowledgment for peer reviewers, editorial board of Journal of Clinical Research and Reports. They show a lot of consideration for us as publishers for our research article “Evaluation of the different factors associated with side effects of COVID-19 vaccination on medical students, Mutah university, Al-Karak, Jordan”, in a very professional and easy way. This journal is one of outstanding medical journal.
Dear Hao Jiang, to Journal of Nutrition and Food Processing We greatly appreciate the efficient, professional and rapid processing of our paper by your team. If there is anything else we should do, please do not hesitate to let us know. On behalf of my co-authors, we would like to express our great appreciation to editor and reviewers.
As an author who has recently published in the journal "Brain and Neurological Disorders". I am delighted to provide a testimonial on the peer review process, editorial office support, and the overall quality of the journal. The peer review process at Brain and Neurological Disorders is rigorous and meticulous, ensuring that only high-quality, evidence-based research is published. The reviewers are experts in their fields, and their comments and suggestions were constructive and helped improve the quality of my manuscript. The review process was timely and efficient, with clear communication from the editorial office at each stage. The support from the editorial office was exceptional throughout the entire process. The editorial staff was responsive, professional, and always willing to help. They provided valuable guidance on formatting, structure, and ethical considerations, making the submission process seamless. Moreover, they kept me informed about the status of my manuscript and provided timely updates, which made the process less stressful. The journal Brain and Neurological Disorders is of the highest quality, with a strong focus on publishing cutting-edge research in the field of neurology. The articles published in this journal are well-researched, rigorously peer-reviewed, and written by experts in the field. The journal maintains high standards, ensuring that readers are provided with the most up-to-date and reliable information on brain and neurological disorders. In conclusion, I had a wonderful experience publishing in Brain and Neurological Disorders. The peer review process was thorough, the editorial office provided exceptional support, and the journal's quality is second to none. I would highly recommend this journal to any researcher working in the field of neurology and brain disorders.
Dear Agrippa Hilda, Journal of Neuroscience and Neurological Surgery, Editorial Coordinator, I trust this message finds you well. I want to extend my appreciation for considering my article for publication in your esteemed journal. I am pleased to provide a testimonial regarding the peer review process and the support received from your editorial office. The peer review process for my paper was carried out in a highly professional and thorough manner. The feedback and comments provided by the authors were constructive and very useful in improving the quality of the manuscript. This rigorous assessment process undoubtedly contributes to the high standards maintained by your journal.
International Journal of Clinical Case Reports and Reviews. I strongly recommend to consider submitting your work to this high-quality journal. The support and availability of the Editorial staff is outstanding and the review process was both efficient and rigorous.
Thank you very much for publishing my Research Article titled “Comparing Treatment Outcome Of Allergic Rhinitis Patients After Using Fluticasone Nasal Spray And Nasal Douching" in the Journal of Clinical Otorhinolaryngology. As Medical Professionals we are immensely benefited from study of various informative Articles and Papers published in this high quality Journal. I look forward to enriching my knowledge by regular study of the Journal and contribute my future work in the field of ENT through the Journal for use by the medical fraternity. The support from the Editorial office was excellent and very prompt. I also welcome the comments received from the readers of my Research Article.
Dear Erica Kelsey, Editorial Coordinator of Cancer Research and Cellular Therapeutics Our team is very satisfied with the processing of our paper by your journal. That was fast, efficient, rigorous, but without unnecessary complications. We appreciated the very short time between the submission of the paper and its publication on line on your site.
I am very glad to say that the peer review process is very successful and fast and support from the Editorial Office. Therefore, I would like to continue our scientific relationship for a long time. And I especially thank you for your kindly attention towards my article. Have a good day!
"We recently published an article entitled “Influence of beta-Cyclodextrins upon the Degradation of Carbofuran Derivatives under Alkaline Conditions" in the Journal of “Pesticides and Biofertilizers” to show that the cyclodextrins protect the carbamates increasing their half-life time in the presence of basic conditions This will be very helpful to understand carbofuran behaviour in the analytical, agro-environmental and food areas. We greatly appreciated the interaction with the editor and the editorial team; we were particularly well accompanied during the course of the revision process, since all various steps towards publication were short and without delay".
I would like to express my gratitude towards you process of article review and submission. I found this to be very fair and expedient. Your follow up has been excellent. I have many publications in national and international journal and your process has been one of the best so far. Keep up the great work.
We are grateful for this opportunity to provide a glowing recommendation to the Journal of Psychiatry and Psychotherapy. We found that the editorial team were very supportive, helpful, kept us abreast of timelines and over all very professional in nature. The peer review process was rigorous, efficient and constructive that really enhanced our article submission. The experience with this journal remains one of our best ever and we look forward to providing future submissions in the near future.
I am very pleased to serve as EBM of the journal, I hope many years of my experience in stem cells can help the journal from one way or another. As we know, stem cells hold great potential for regenerative medicine, which are mostly used to promote the repair response of diseased, dysfunctional or injured tissue using stem cells or their derivatives. I think Stem Cell Research and Therapeutics International is a great platform to publish and share the understanding towards the biology and translational or clinical application of stem cells.
I would like to give my testimony in the support I have got by the peer review process and to support the editorial office where they were of asset to support young author like me to be encouraged to publish their work in your respected journal and globalize and share knowledge across the globe. I really give my great gratitude to your journal and the peer review including the editorial office.
I am delighted to publish our manuscript entitled "A Perspective on Cocaine Induced Stroke - Its Mechanisms and Management" in the Journal of Neuroscience and Neurological Surgery. The peer review process, support from the editorial office, and quality of the journal are excellent. The manuscripts published are of high quality and of excellent scientific value. I recommend this journal very much to colleagues.
Dr.Tania Muñoz, My experience as researcher and author of a review article in The Journal Clinical Cardiology and Interventions has been very enriching and stimulating. The editorial team is excellent, performs its work with absolute responsibility and delivery. They are proactive, dynamic and receptive to all proposals. Supporting at all times the vast universe of authors who choose them as an option for publication. The team of review specialists, members of the editorial board, are brilliant professionals, with remarkable performance in medical research and scientific methodology. Together they form a frontline team that consolidates the JCCI as a magnificent option for the publication and review of high-level medical articles and broad collective interest. I am honored to be able to share my review article and open to receive all your comments.
“The peer review process of JPMHC is quick and effective. Authors are benefited by good and professional reviewers with huge experience in the field of psychology and mental health. The support from the editorial office is very professional. People to contact to are friendly and happy to help and assist any query authors might have. Quality of the Journal is scientific and publishes ground-breaking research on mental health that is useful for other professionals in the field”.
Dear editorial department: On behalf of our team, I hereby certify the reliability and superiority of the International Journal of Clinical Case Reports and Reviews in the peer review process, editorial support, and journal quality. Firstly, the peer review process of the International Journal of Clinical Case Reports and Reviews is rigorous, fair, transparent, fast, and of high quality. The editorial department invites experts from relevant fields as anonymous reviewers to review all submitted manuscripts. These experts have rich academic backgrounds and experience, and can accurately evaluate the academic quality, originality, and suitability of manuscripts. The editorial department is committed to ensuring the rigor of the peer review process, while also making every effort to ensure a fast review cycle to meet the needs of authors and the academic community. Secondly, the editorial team of the International Journal of Clinical Case Reports and Reviews is composed of a group of senior scholars and professionals with rich experience and professional knowledge in related fields. The editorial department is committed to assisting authors in improving their manuscripts, ensuring their academic accuracy, clarity, and completeness. Editors actively collaborate with authors, providing useful suggestions and feedback to promote the improvement and development of the manuscript. We believe that the support of the editorial department is one of the key factors in ensuring the quality of the journal. Finally, the International Journal of Clinical Case Reports and Reviews is renowned for its high- quality articles and strict academic standards. The editorial department is committed to publishing innovative and academically valuable research results to promote the development and progress of related fields. The International Journal of Clinical Case Reports and Reviews is reasonably priced and ensures excellent service and quality ratio, allowing authors to obtain high-level academic publishing opportunities in an affordable manner. I hereby solemnly declare that the International Journal of Clinical Case Reports and Reviews has a high level of credibility and superiority in terms of peer review process, editorial support, reasonable fees, and journal quality. Sincerely, Rui Tao.
Clinical Cardiology and Cardiovascular Interventions I testity the covering of the peer review process, support from the editorial office, and quality of the journal.
